High definition camera and image recognition system for criminal identification

ABSTRACT

A system for high definition (HD) image recognition of criminals is disclosed. The system includes a plurality of cameras, an image recognition server, investigator user devices, a computing device, a database, and a network. At least one processor of the image recognition server is configured to receive a plurality of photographs of a first individual, perform image processing of the plurality of photographs to extract a first set of physical features, store feature data regarding the first set of physical features in the database, receive suspect data regarding a suspected individual from a first investigator user device, match the suspect data with the feature data stored in the database, and transmit an alert to the computing device in the prison, wherein the alert activates a mobile application on each investigator user device to display match data identifying the suspected individual as the first individual based on the feature data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/787,130 filed on Oct. 18, 2017, which is incorporated by referenceherein in its entirety.

BACKGROUND Field

The disclosure relates to a high definition (HD) camera and imagerecognition system and methods for identifying individuals involved incriminal activities during booking and investigations.

Background

Law enforcement members routinely seek information regarding suspectsthat may be relevant to on-going investigations, especially if thesuspects have previously been incarcerated. For example, a policeofficer arrests an individual involved in a criminal activity andtransports the individual to a prison, where information regarding theindividual is collected during a booking process. The individual is thenasked to provide identification, including his or her full name,address, contact information, and the like, and one or more bookingphotographs or “mugshots” are taken of the individual. However,individuals often fail to provide proper identification to arrestingofficers, and current technologies at prisons are limited in quicklyidentifying criminals and previously incarcerated suspects fortime-sensitive investigations.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate embodiments of the present disclosureand, together with the description, further serve to explain theprinciples of the disclosure and to enable a person skilled in thepertinent art to make and use the embodiments.

FIG. 1 illustrates a block diagram of a high definition (HD) camera andimage recognition system, according to embodiments of the presentdisclosure.

FIGS. 2A and 2B illustrate example diagrams of configurations of a highdefinition (HD) camera system arranged in a room of a prison, accordingto embodiments of the present disclosure.

FIG. 3 illustrates a block diagram of an image recognition server in theHD camera and image recognition system, according to embodiments of thepresent disclosure.

FIG. 4 illustrates a user interface of an investigator user device,according to embodiments of the present disclosure.

FIG. 5 illustrates a flowchart diagram of a method for determiningidentities of suspected individuals by matching features of arrestedindividuals, according to embodiments of the present disclosure.

FIG. 6 illustrates a flowchart diagram of a method for identifyingfeatures of arrested individuals through image processing, according toembodiments of the present disclosure.

FIG. 7 illustrates a block diagram of a general purpose computer thatmay be used to perform various aspects of the present disclosure.

The present disclosure will be described with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements. Additionally, the left mostdigit(s) of a reference number identifies the drawing in which thereference number first appears.

DETAILED DESCRIPTION

The following Detailed Description refers to accompanying drawings toillustrate exemplary embodiments consistent with the disclosure.References in the Detailed Description to “one exemplary embodiment,”“an exemplary embodiment,” “an example exemplary embodiment,” etc.,indicate that the exemplary embodiment described may include aparticular feature, structure, or characteristic, but every exemplaryembodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same exemplary embodiment. Further, when a particularfeature, structure, or characteristic is described in connection with anexemplary embodiment, it is within the knowledge of those skilled in therelevant art(s) to affect such feature, structure, or characteristic inconnection with other exemplary embodiments whether or not explicitlydescribed.

The exemplary embodiments described herein are provided for illustrativepurposes, and are not limiting. Other exemplary embodiments arepossible, and modifications may be made to the exemplary embodimentswithin the spirit and scope of the disclosure. Therefore, the DetailedDescription is not meant to limit the invention. Rather, the scope ofthe invention is defined only in accordance with the following claimsand their equivalents.

Embodiments may be implemented in hardware (e.g., circuits), firmware,software, or any combination thereof. Embodiments may also beimplemented as instructions stored on a machine-readable medium, whichmay be read and executed by one or more processors. A machine-readablemedium may include any mechanism for storing or transmitting informationin a form readable by a machine (e.g., a computing device). For example,a machine-readable medium may include read only memory (ROM); randomaccess memory (RAM); magnetic disk storage media; optical storage media;flash memory devices; electrical, optical, acoustical or other forms ofpropagated signals (e.g., carrier waves, infrared signals, digitalsignals, etc.), and others. Further, firmware, software, routines,instructions may be described herein as performing certain actions.However, it should be appreciated that such descriptions are merely forconvenience and that such actions in fact result from computing devices,processors, controllers, or other devices executing the firmware,software, routines, instructions, etc. Further, any of theimplementation variations may be carried out by a general purposecomputer, as described below.

For purposes of this discussion, any reference to the term “module”shall be understood to include at least one of software, firmware, orhardware (such as one or more circuit, microchip, or device, or anycombination thereof), and any combination thereof. In addition, it willbe understood that each module may include one, or more than one,component within an actual device, and each component that forms a partof the described module may function either cooperatively orindependently of any other component forming a part of the module.Conversely, multiple modules described herein may represent a singlecomponent within an actual device. Further, components within a modulemay be in a single device or distributed among multiple devices in awired or wireless manner.

The following Detailed Description of the exemplary embodiments will sofully reveal the general nature of the invention that others can, byapplying knowledge of those skilled in relevant art(s), readily modifyand/or adapt for various applications such exemplary embodiments,without undue experimentation, without departing from the spirit andscope of the disclosure. Therefore, such adaptations and modificationsare intended to be within the meaning and plurality of equivalents ofthe exemplary embodiments based upon the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by those skilled in relevant art(s) in light of theteachings herein.

Overview

Law enforcement members routinely seek information regarding suspectsthat may be relevant to on-going investigations, especially if thesuspects have previously been incarcerated. For example, a policeofficer arrests an individual involved in a criminal activity andtransport the individual to a prison, where information regarding theindividual is collected during a booking process. The individual isasked to provide identification, including his or her full name,address, contact information, and the like, and one or more bookingphotographs or “mugshots” are taken of the individual. However,individuals often fail to provide proper identification to arrestingofficers, and identifying individuals who have previously beenincarcerated based on conventional mugshots is inefficient because ofthe modification of physical appearance.

For example, criminals frequently modify their physical appearance orphysical features by getting numerous tattoos all over their bodies,such as on their faces, chests, arms, legs or the like. In some cases,criminals obtain specific tattoos that are gang-affiliated to showothers their gang affiliations. Criminals also remove tattoos and/or addnew tattoos to non-tattooed regions of their bodies. In another example,criminals modify their physical features by getting plastic surgery orhaving operations performed to alter their appearances. Such individualsmight not be recognizable by previous mugshots during booking becausetheir physical appearance may have drastically changed since the lasttime their mugshots were taken, such as when previously incarcerated.

Additionally, current technologies at prisons are limited in providingprompt and efficient identification of criminals from conventionalmugshots. Current systems implemented by jurisdictions for identifyingcriminals do not include sophisticated technologies that are needed fordetecting modified physical appearances of criminals through enhancedimage recognition techniques. In some cases, cameras utilized byjurisdictions are of a lower resolution and quality, and mugshots orimages of criminals captured by such cameras do not provide a full andcomprehensive view of the physical attributes of each individual.

For example, cameras conventionally used by prisons are unable tocapture or photograph in high resolution various changes that anindividual may acquire after a previous booking, such as a plurality oftattoos that have been added to the individual's body. Furthermore,arresting officers or jail personnel members who are utilizing camerasto take mugshots of individuals at the prison often take the photographsin an area of the booking room that might not have sufficient light or aproper background to prevent photographs captured by the camera frombeing underexposed or overexposed. Such deficient photographs capturedby conventional cameras do not provide a complete view of a criminal,including fully depicting identifying marks such as tattoos on acriminal's body.

Additionally or alternatively, jurisdictions may rely on fingerprintmatching and/or DNA analysis to identify a criminal by taking anindividual's fingerprints and/or a blood sample and searching for amatch. However, such technologies take a longer amount of time toprocess the fingerprints and/or sample and find a match, than the time aphotograph-based identification would take. Identifying criminals asquickly as possible in a short amount of time is of paramountimportance, particularly for recognizing and pursuing possible suspectsin ongoing investigations that are time-sensitive. Accordingly, there isa need for new technologies, systems, and methods for an improved andefficient identification of criminals during booking based on themodification of physical appearances of criminals.

High Definition (HD) Camera and Image Recognition System

FIG. 1 illustrates a block diagram of a high definition (HD) camera andimage recognition system 100, according to embodiments of the presentdisclosure. The HD camera and image recognition system 100 includes aprison 102, an image recognition server 120, and a plurality ofinvestigator user devices 142 (e.g., investigator user devices 142 a-142n) that are communicatively coupled via a network 110. In thisdisclosure, prison 102 refers to a local, county, state, and/or federalprison, jail, or correctional facility, in which individuals that havebeen arrested are booked and processed before confinement. The prison102 further includes a plurality of cameras 132 (e.g., cameras 132 a-132n). The plurality of cameras 132 include high definition (HD) camerasthat are located in a room of the prison 102, where arrested individualsare photographed for identification purposes. For example, the cameras132 are arranged in various configurations in the room of the prison 102in order to capture photographs of arrested individuals from a pluralityof angles for a comprehensive view of the physical attributes of eacharrested individual, as will be described in further detail below.

The prison 102 further includes a plurality of computing devices 134(e.g., computing devices 134 a-134 n). The plurality of computingdevices 134 include one or more desktop computers, tablet computers,laptop computers, computer terminals, wireless devices, computerworkstations, or the like, which are utilized by one or more employees,officers, or personnel in the prison 102. In some embodiments, thecomputing devices 134 include one or more central processing units(CPU), system memory (e.g., RAM), data storage, an input device, such asa keyboard, mouse, camera, and/or microphone, a monitor for displaying auser interface, a network interface card, and/or a modem that providesnetwork connectivity and communication. The computing devices 134 arelocated locally within the prison 102, such as in a designated area orroom of the prison 102. For example, the computing devices 134 arelocated in a room or area that is separate from the room where thecameras 132 are located.

In some embodiments, the computing devices 134 are configured to receivephotographs captured by one or more cameras 132, modify or convert theformat of each photograph, and transmit a formatted version of eachphotograph to the image recognition server 120 via network 110. Thenetwork 110 includes at least one of a private network, personal areanetwork (PAN), local area network (LAN), wide area network (WAN), or theInternet. The image recognition server 120 includes any number ofservers, computers, and/or devices for receiving photographs captured byone or more cameras 132 directly from the cameras 132 or from one ormore computing devices 134. The image recognition server 120 isconfigured to process photographs of arrested individuals and utilizepattern recognition and image analysis techniques to match photographsof arrested individuals with photographs of criminals or suspects, aswill be described in further detail below.

In some embodiments, the image recognition server 120 is furtherconfigured to communicate with and transmit alerts to computing devices134 and/or a plurality of investigator user devices 142 regardingmatching photographs. The plurality of investigator user devices 142include at least one of a desktop computer, a tablet computer, a laptopcomputer, a computer terminal, a wireless device, or a mobile devicecapable of viewing a user interface. For example, the investigator userdevices 142 include one or more central processing units (CPU), systemmemory (e.g., RAM), data storage, an input device, such as a keyboard,mouse, camera, and/or microphone, a monitor for displaying a userinterface, a network interface card, and/or a modem that providesnetwork connectivity and communication. In some embodiments, theinvestigator user devices 142 include user devices (e.g., mobiledevices) that are each associated with or used by an individual, such asan investigator, detective, prosecutor, police officer, or administratorwho is authorized to investigate suspects or individuals who areinvolved in criminal activities. In some cases, each investigator userdevice 142 further includes a mobile application installed in a memoryof the investigator user device 142, in which the mobile application isconfigured to display alerts regarding whether or not a suspectedindividual has been identified based on matching photographs, as will bedescribed in further detail below.

In additional embodiments, the connection between the plurality ofcameras 132 and the plurality of computing devices 134 in prison 102,the network 110, the image recognition server 120, and the plurality ofinvestigator user devices 142 is a wireless connection (e.g.,Bluetooth™, Wi-Fi connection, or the like) or a wired connection (e.g.,Ethernet, universal serial bus (USB), or the like).

HD Camera System

FIGS. 2A and 2B illustrate example diagrams of configurations of a highdefinition (HD) camera system arranged in a room of a prison, accordingto embodiments of the present disclosure. In particular, FIG. 2Aillustrates a first configuration of the high definition (HD) camerasystem in which there are four cameras 204 arranged within the perimeterof a room 200 in the prison and configured to photograph an individual202. In some embodiments, cameras 204 and room 200 represent exemplaryembodiments of the plurality of cameras 132 (e.g., cameras 132 a-132 n)and a room in prison 102 in FIG. 1 .

The cameras 204 include high definition (HD) digital cameras that areconfigured to generate HD quality photographs. For example, the cameras204 capture HD quality photographs with high resolutions, such as1920×1080 pixels, 1440×1080 pixels, 1280×720 pixels, or the like. Insome examples, the cameras 204 capture photographs with ultra-highdefinition resolutions, such as a resolution of 3840×2160 pixels or thelike. In some embodiments, the cameras 204 comprise different cameras ofvarying resolutions to capture photographs of the individual 202 withboth low and high resolutions. For example, photographs of lowresolutions from one or more cameras 204 are processed first by theimage recognition server 120, and photographs of high resolutions fromone or more cameras 204 are processed by the image recognition server120 to obtain additional details after processing of the photographs oflow resolutions.

Each camera 204 is configured to capture a plurality of photographs ofthe individual 202 standing in the center of the room 200. Theindividual 202 is an arrested individual, suspect, or criminal who hasbeen arrested for criminal activities and brought to the prison by anarresting officer for booking and processing. After providingidentification information to one or more officers at the prison, theindividual 202 enters the room 200 through an entrance 206 on the sideof the room 200 and poses for photographs that are captured by theplurality of cameras 204. For example, the individual 202 poses inlimited articles of clothing in front of the cameras 204. In anotherexample, the individual 202 poses nude in front of the cameras 204, andnudity is censored (e.g., by blurring, pixilation, or solid colors) fromthe photographs during processing by the image recognition server 120.In some embodiments, the room 200 has similar dimensions to a photobooth and is rectangular in shape. In other embodiments, the room 200 issquare-shaped with dimensions (width×length) of 4 feet×4 feet, 5 feet×5feet, 6 feet×6 feet, or the like.

As shown in FIG. 2A, the cameras 204 are mounted on the walls of theroom 200, in which each camera 204 is mounted in the center of eachwall. In some embodiments, the cameras 204 and the equipment utilized tomount the cameras 204 in the room 200 are referred to herein as a highdefinition (HD) camera system. The cameras 204 mounted to the walls arefixed or stationary, and each camera 204 takes at least one photographof the individual 202 at or around the same time. For example, thecameras 204 are simultaneously triggered to capture photographs at oraround the same time. In some cases, the cameras 204 are simultaneouslytriggered by receiving a command transmitted from a computing device 134in the prison 102. In response to receiving the command, the fourcameras 204 each triggered to capture at least one photograph of theindividual 202 at or around the same time, resulting in at least fourphotographs with different views depicting the front side, back side,right side, and left side of the individual 202. In some embodiments,each camera 204 captures a burst of photographs comprising multiplephotographs (e.g., 5 photographs, 10 photographs, or anotherpredetermined number of photographs) of the individual 202 that aretaken within a predetermined time period (e.g., within 2 seconds, 5seconds, 30 seconds, 1 minutes, or the like). Thus, the cameras 204capture a burst or a set of photographs for each view of the individual202, resulting in multiple photographs from the front side, back side,right side, and left side of the individual 202. In some cases, thecameras 204 are programmed with one or more predefined rules set by anadministrator of the prison to capture any number of photographs asdesired. In additional or alternative embodiments, the cameras 204 areconfigured to capture one or more videos of the individual 202 at oraround the same time as the time that the photographs are taken.Although only four cameras 204 are shown in FIG. 2A, it is understoodthat there can be any number of cameras 204 in the room as a part of theHD camera system.

FIG. 2B illustrates a second configuration of the high definition (HD)camera system in which there is one camera 214 arranged within thecircumference of a room 210 in the prison and configured to photographan individual 212. The camera 214 is track mounted in the room 210, suchthat the camera 214 is arranged to move around the room 210 on a trackand capture photographs while the camera 214 is in motion and/or whilethe camera 214 is stopped at predetermined locations along the track. Insome cases, the high definition (HD) camera system includes a mountedand/or motorized track for the camera 214 that is mounted on the wallsand/or ceiling of the room 210. For example, the motorized track for thecamera 214 is configured to control camera movements for shifting leftto right and vice versa, as well as pan and tilt movements for thecamera 214.

In some embodiments, camera 214 and room 210 represent exemplaryembodiments of the plurality of cameras 132 (e.g., cameras 132 a-132 n)and a room in prison 102 in FIG. 1 . The camera 214 is a high definition(HD) digital camera that is configured to generate HD qualityphotographs. For example, the camera 214 captures HD quality photographswith high resolutions, such as 1920×1080 pixels, 1440×1080 pixels,1280×720 pixels, or the like. In some examples, the camera 214 capturesphotographs with ultra-high definition resolutions, such as a resolutionof 3840×2160 pixels or the like. In some embodiments, the camera 214capture photographs of the individual 212 with varying resolutions,including both low and high resolutions. For example, photographs of lowresolutions from the camera 214 are processed first by the imagerecognition server 120, and photographs of high resolutions from thecamera 214 are processed by the image recognition server 120 to obtainadditional details after processing of the photographs of lowresolutions.

The camera 214 is configured to capture a plurality of photographs ofthe individual 212 standing in the center of the room 210. In someembodiments, the individual 212 is the same arrested individual,suspect, or criminal as individual 202 in FIG. 2A. After providingidentification information to one or more officers at the prison, theindividual 212 enters the room 210 through an entrance 216 on the sideof the room 210 and poses for photographs that are captured by thecamera 214. For example, the individual 212 poses in limited articles ofclothing in front of the camera 214. In another example, the individual212 poses nude in front of the camera 213, and nudity is censored (e.g.,by blurring, pixilation, or solid colors) from the photographs duringprocessing by the image recognition server 120 In some embodiments, theroom 210 comprises a circular or oval shape with a diameter of 4 feet, 5feet, 6 feet, or the like. In additional embodiments, the room 210comprises any shape with a track that is mounted around the room, suchthat the camera 214 moves around the room 210 along the track. As shownin FIG. 2B, the camera 214 is arranged at a location on a track mountedto the walls of the room 210, such that the arrangement allows thecamera 214 to pan or move around the circumference of the room 210(e.g., within the walls of the room 210) to capture photographs and/orvideos of the individual 212 from a plurality of angles around the room210. In some embodiments, the camera 214 rotates around the room 210 onthe track to capture one or more high resolution panoramic photographsor 360° photographs.

In some embodiments, the camera 214 comprises a rotating HD camera thatis mounted on vertical and horizontal axes (e.g., one or more tracks) ofa wall of the room 210, such that the camera 214 can move vertically andhorizontally to obtain accurate positioning of the individual 212. Forexample, the camera 214 shifts in position vertically (e.g., up or down)by a predetermined distance on the track based on detecting the heightof an individual 212, such that the camera 214 can capture accuratephotographs of the individual 212 from a proper vantage point. Thecamera 214 also rotates or shifts in position horizontally (e.g., leftor right) to capture a plurality of photographs of the individual 212from different angles around the room 210. For example, the camera 214is mounted to camera equipment (e.g., a camera dolly or another device)that allows rotation of the camera 214 around the room 210. In someembodiments, the camera 214 and the equipment utilized to move, pivot,or rotate the camera 214 in the room 210 are referred to herein as arotating high definition (HD) camera system.

In some cases, the camera 214 and the accompanying equipment areautomated, such that the camera 214 moves around the room 210 capture apredetermined number of photographs within a predetermined time intervalfrom various angles. For example, the camera 214 captures 2 photographs,5 photographs, 10 photographs, or another number of photographs everyminute, every two minutes, or within another time interval from variousangles around the room 210, in which the camera 214 moves in 15-degree,45 degree, or 90 degree increments on a track around the room 210. Insome embodiments, the camera 214 also rotates clockwise orcounter-clockwise around the room 210 at a predetermined room 210. Byobtaining photos from different angles around the room 210, the camera214 obtains comprehensive photographs that provide detailed views of thephysical attributes of the individual 212, including tattoos, piercings,scars, brandings, surgical modifications, and other physical markings onthe individual's body. In some cases, the camera 214 is programmed withone or more predefined rules set by an administrator of the prison tocapture any number of photographs as desired. Although only camera 214is shown in FIG. 2B, it is understood that there can be any number ofcameras 214 in the room as a part of the rotating HD camera system.

Image Recognition Server

FIG. 3 illustrates a block diagram of the image recognition server 300,according to embodiments of the present disclosure. Image recognitionserver 300 represents an exemplary embodiment of image recognitionserver 120 in FIG. 1 . Image recognition server includes one or moreservers or other types of computing devices that can be embodied in anynumber of ways. For instance, the modules, other functional components,and data can be implemented on a single server, a cluster of servers, aserver farm or data center, a cloud-hosted computing service, and soforth, although other computer architectures can additionally oralternatively be used.

Further, while the figures illustrate the components and data of theimage recognition server 300 as being present in a single location,these components and data may alternatively be distributed acrossdifferent computing devices and different locations in any manner.Consequently, the functions may be implemented by one or more computingdevices, with the various functionality described above distributed invarious ways across the different computing devices. Multiple imagerecognition servers 300 may be located together or separately, andorganized, for example, as virtual servers, server banks and/or serverfarms. The described functionality may be provided by the servers of asingle entity or enterprise, or may be provided by the servers and/orservices of multiple different entities or enterprises.

In the illustrated example, the image recognition server 300 includesone or more processors 302, one or more computer-readable media 304, andone or more communication interfaces 306. Each processor 302 is a singleprocessing unit or a number of processing units, and may include singleor multiple computing units or multiple processing cores. Theprocessor(s) 302 can be implemented as one or more microprocessors,microcomputers, microcontrollers, digital signal processors, centralprocessing units, state machines, logic circuitries, and/or any devicesthat manipulate signals based on operational instructions. For instance,the processor(s) 302 may be one or more hardware processors and/or logiccircuits of any suitable type specifically programmed or configured toexecute the algorithms and processes described herein. The processor(s)302 can be configured to fetch and execute computer-readableinstructions stored in the computer-readable media 304, which canprogram the processor(s) 302 to perform the functions described herein.

The computer-readable media 304 include volatile and nonvolatile memoryand/or removable and non-removable media implemented in any type oftechnology for storage of information, such as computer-readableinstructions, data structures, program modules, or other data. Suchcomputer-readable media 304 include, but are not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, optical storage, solidstate storage, magnetic tape, magnetic disk storage, network attachedstorage, storage area networks, cloud storage, or any other medium thatcan be used to store the desired information and that can be accessed bya computing device. Depending on the configuration of the imagerecognition server 300, the computer-readable media 304 may be a type ofcomputer-readable storage media and/or may be a tangible non-transitorymedia to the extent that when mentioned, non-transitorycomputer-readable media exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

The computer-readable media 304 is used to store any number offunctional components that are executable by the processors 302. In manyimplementations, these functional components comprise instructions orprograms that are executable by the processors and that, when executed,specifically configure the one or more processors 302 to perform theactions attributed above to the image recognition server 300. Inaddition, the computer-readable media 304 store data used for performingthe operations described herein.

In the illustrated example, the computer-readable media 304 furtherincludes criminal profile module 308, image processing module 310,feature extraction module 312, hashing module 314, booking database 316,pattern recognition module 318, and notification module 328. Criminalprofile module 308 obtains and stores profile information for eachcriminal in the prison. In some embodiments, criminal profile module 308obtains profile information related to arrested individuals from one ormore of (a) a jail management system (JMS) or an offender managementsystem (OMS) operated by the jurisdiction of the prison or correctionalfacility, (b) public database containing information on the individuals,or (c) a questionnaire provided by a web page or booking information.For example, profile information obtained by criminal profile module 308includes personal information and booking information for each criminal,such as names, previous residences or correctional facilities, familymembers, languages, previous arrest history, fingerprint information,information regarding witnesses or other individuals pertinent to thecriminal's case, and the like. Based on this profile information,criminal profile module 308 generates a criminal profile for eachindividual arrested and booked at the prison.

In some embodiments, criminal profile module 308 communicates with imageprocessing module 310 to associate a criminal profile that correspondsto each arrested individual with one or more photographs captured by thecameras in the prison (e.g., cameras 132, 204, and/or 214). For example,criminal profile module 308 receives data regarding photographs ofarrested individuals from image processing module 310 and stores thedata regarding the photographs with the criminal profile for eacharrested individual within the criminal profile module 308 and/orbooking database 316. In some embodiments, each criminal profileincludes one or more photographs identifying the arrested individual. Inother embodiments, each criminal profile includes one or more links tothe location(s) of photographs identifying the arrested individuals, inwhich the photographs are stored in booking database 316.

Image processing module 310 receives, processes, and manages a pluralityof photographs captured by one or more HD cameras in a prison. Forexample, image processing module 310 receives a plurality of photographsfor each arrested individual during booking at prison 102. In somecases, image processing module 310 receives the photographs as digitalimages directly from at least one camera, such as cameras 132, cameras204, or camera 214, whereas in other cases, image processing module 310receives photographs captured by the cameras as digital images from acomputing device (e.g., computing device 134) in the prison 102. Forexample, image processing module 310 receives photographs from computingdevice 134 after the photographs have been modified or converted to adifferent format.

In such cases, image processing module 310 performs image processing ofa formatted version of each photograph taken by the one or more HDcameras. For example, image processing module 310 is configured toperform format conversion, decryption, and/or encryption of photographsreceived from the cameras in the HD camera and image recognition system100. Some examples of image formats for the photographs include JPEG,JPEG-XR, TIFF, BMP, PNG, GIF, or the like. In additional embodiments,image processing module 310 also receives and manages metadata for eachphotograph, in which metadata includes data associated with eachphotograph, such as a date and time that each photograph is captured, atype of encoding (e.g., photo compression), a rate of encoding (e.g.,compression rate), which camera(s) were utilized to capture thephotographs, one or more locations of the camera(s) utilized to captureeach photograph, and other attributes or characteristics of eachphotograph.

Upon receiving digital images corresponding to the plurality ofphotographs (e.g., via camera 132 or computing device 134), the imageprocessing module 310 utilizes one or more digital image processingalgorithms to obtain image data for each photograph of an arrestedindividual. Examples of digital image processing algorithms that areapplied to each photograph include Fourier analysis, image segmentation,edge detection, compression, and other techniques. In some cases, imageprocessing module 310 also applies additional formatting to eachphotograph, including sharpening, zooming, blurring, cropping, rotating,and other editing processes. In some embodiments, image processingmodule 310 performs pre-processing of photographs prior to furtherprocessing and analysis of the photographs by the feature extractionmodule 312.

Feature extraction module 312 performs further image analysis ofphotographs of the arrested individuals in the prison. In particular,feature extraction module 312 receives a plurality of photographs fromimage processing module 310 after the photographs have beenpre-processed, and feature extraction module 312 analyzes thephotographs to identify a set of features from each photograph ofarrested individuals. In some embodiments, feature extraction module 312analyzes a plurality of pixels of each photograph to identify pixelintensity values. For example, feature extraction module 312 analyzesintensity values of pixels in a grayscale, binary, or RGB digital imagecorresponding to each photograph. For each photograph, the featureextraction module 312 detects regions of pixels that differ in colorand/or intensity values by one or more predetermined threshold valuesfrom adjacent regions of pixels.

The feature extraction module 312 compares intensity values of pixelswith intensity values of neighboring pixels to identify similarities anddifferences between regions in each photograph. In some cases, featureextraction module 312 detects that a region of neighboring pixels hassimilar intensity values with an adjacent region of neighboring pixels,in which the similar color and/or intensity values indicates thedetection of flesh tones (e.g., untattooed or unmarked skin) of thearrested individual's body represented in the two regions of pixels. Thefeature extraction module 312 continues to analyze and compare eachregion of pixels with adjacent regions until the color and/or intensityvalues of a first region of pixels differ from the color and/orintensity values of a second region of pixels by at least one or morepredetermined threshold values, in which the differing color and/orintensity values indicate the detection of a tattoo, piercing, branding,surgical modification, physical marking, and/or scar on the arrestedindividual's body.

Upon detecting these differences, the feature extraction module 312identifies a first set of features comprising data regarding visualattributes of the arrested individual, in which the visual attributesinclude tattoos, piercings, scars, brandings, surgical modifications,and/or physical markings on the body of the arrested individual. Thedata regarding the visual attributes includes one or more valuescorresponding to shape, color, size, position, intensity, and the likeof the visual attributes of the arrested individual. In some cases, thefeature extraction module 312 utilizes machine learning models toidentify and extract sets of features from one or more photographs ofeach arrested individual. The feature extraction module 312 furthercommunicates with hashing module 314 to provide the extracted featuredata regarding the set of features for each arrested individual.

Hashing module 314 manages data transformation, encryption, and storageof the set of features for each arrested individual in the prison. Inparticular, hashing module 314 receives feature data regarding a set offeatures of each arrested individual from feature extraction module 312and performs cryptographic hashing to transform the feature data througha hash function for security purposes. For example, hashing module 314generates a hash value (e.g., a cryptographic hash) for each feature ofan arrested individual by transforming the data to a fixed-length valuerepresenting the feature. The hashing module 314 further stores the hashvalue for each feature in booking database 316. In some embodiments, thehashing module 314 communicates with booking database 316, patternrecognition module 318, and/or notification module 328 to compute hashvalues and compare values for matches of feature data using computedhash values.

Booking database 316 comprises any number of databases and/or servers,and stores and organizes data in one or more relational databases.Booking database 316 runs a database management system, such as MYSQL™,to provide an example. In particular, booking database 316 receives oneor more photographs of arrested individuals from image processing module310, encrypts the photographs for security purposes, and generatesrecords for each arrested individual with the encrypted photographs.Booking database 316 also receives and stores hash values from hashingmodule 314, such as feature data that has been cryptographically hashed.In some embodiments, booking database includes organized data, such ascriminal profile information, arrest records, fingerprint data,photographs, feature data, and the like, in which the data is indexedand linked to allow access to data for criminal profile module 308,image processing module 310, feature extraction module 312, hashingmodule 314, pattern recognition module 318, and notification module 328.

In additional embodiments, booking database 316 includes a library ordata repository storing a plurality of records for reference dataindicating known gang-affiliated tattoos, piercings, scars, brandings,surgical modifications, and/or physical markings. For example, eachrecord includes at least one of a name of a particular gang, variousimages, symbols, signs, and/or slogans associated with the particulargang, and a corresponding description of tattoo(s) and/or physicalmarking(s) associated with the particular gang. In some cases, thereference data indicating known gang-affiliated tattoos and physicalmarkings are extracted and compiled from previous investigations and/orprevious arrest records and stored in booking database 316.

Pattern recognition module 318 manages matching of feature data ofarrested individuals with suspects involved in criminal activities. Insome embodiments, pattern recognition module 318 receives suspect datafrom one or more investigator user devices 142, in which the suspectdata includes data regarding at least one of a shape, color, size,position, or intensity of a tattoo or identifying mark of a suspect. Insome cases, the suspect data includes at least one of a photograph ofthe suspect and/or a description of the suspect provided by aninvestigator. In particular, pattern recognition module 318 compares thesuspect data with the feature data stored in the booking database 316and determines whether the suspect data matches feature datacorresponding to any arrested individual in the prison. For example,pattern recognition module 318 compares a number of data points in thesuspect data with a number of data points in the feature data by usingat least one of pattern recognition algorithms, matching algorithms,biometric tattoo recognition algorithms, Hidden Markov models, and thelike.

The pattern recognition module 318 performs the matching and alsodetermines a confidence value that reflects the degree of the matchbetween suspect data of a suspected individual and feature data of anarrested individual. A higher confidence value indicates a greaterdegree of matching than a lower confidence value. For example, thepattern recognition module 318 determines a high confidence value uponmatching feature data representing an arrested individual's tattoo orphysical marking with a suspected individual's tattoo or physicalmarking in a sketch, a photograph, or a video frame taken at a crimescene investigation or from a camera recording during the criminalactivity. After a match between the suspect data and the feature datahas been detected, the pattern recognition module 318 communicates withthe notification module 328 to provide information regarding the match,including match data identifying the suspected individual as apreviously arrested individual. The pattern recognition module 318 alsocommunicates with notification module 328 if a match has not beendetected between the suspect data and the feature data in the bookingdatabase 316.

In additional embodiments, pattern recognition module 318 identifiesgang affiliations of arrested individuals based on feature data receivedfrom the feature extraction module 312. For example, a criminal may havea specific image, symbol, sign, or slogan tattooed on their body torepresent a gang affiliation. The pattern recognition module 318identifies an arrested individual's gang affiliation by analyzingfeature data indicating one or more tattoos and/or physical markings ofthe arrested individual and recognizing a gang affiliation of the one ormore gang-affiliated tattoos and/or physical markings by applyingpattern recognition algorithms. In some cases, the pattern recognitionmodule 318 utilizes one or more records of known gang-affiliated tattoosand/or physical markings in booking database 316 to determine that oneor more tattoos and/or physical markings of an arrested individualmatches images, symbols, signs, or a slogan of a particular gang. Thepattern recognition module 318 further communicates with notificationmodule 328 to provide information regarding the gang affiliations ofarrested individuals.

Notification module 328 manages the generation and transmission of oneor more alerts and/or notifications for image recognition of criminals.In particular, notification module 328 receives information regardingdetected matches for suspected individuals and/or gang affiliationinformation from pattern recognition module 318 and generatescorresponding notifications and/or alerts based on the receivedinformation. For example, if the notification module 328 receivesinformation from pattern recognition module 318 indicating that a matchhas not been detected between the suspect data and the feature data inthe booking database 316, then the notification module 328 transmits analert to one or more computing devices 134 in the prison 102, in whichthe alert indicates that no match has been found. In other cases,notification module 328 receives information from pattern recognitionmodule 318, in which the information indicates that a match has beendetected between suspect data and feature data corresponding to apreviously arrested individual. In response to receiving theinformation, notification module 328 transmits an alert to one or morecomputing devices 134 in the prison 102.

In some embodiments, transmission of the alert and/or delivery of thealert to the one or more computing devices 134 activates a mobileapplication installed on each investigator user device 142 to execute oneach investigator user device 142, and the mobile application opens upon the user interface of each investigator user device 142 and displaysa corresponding alert. In some cases, notification module 328communicates with the mobile application installed on the one or moreinvestigator user devices 142. For example, the notification module 328provides match data shown in the alert on the investigator user device142, in which the match data identifies a suspected individual as apreviously arrested individual and includes at least one of a name,photograph, address, or other information corresponding to thepreviously arrested individual.

In additional embodiments, notification module 328 receivesgang-affiliation data from the pattern recognition module 318, in whichthe gang-affiliation data indicates that an arrested individual isassociated with a particular gang based on identification of his or hertattoo(s) and/or physical marking(s). Upon receiving thegang-affiliation data, notification module 328 transmits one or morenotifications and/or messages to computing devices 134 in prison 102, inwhich the notifications and/or messages notify users of the computingdevices 134 (e.g., employees, officers, or personnel in prison 102) thatthe arrested individual is associated with a particular gang. Based onthe gang-affiliation of the arrested individual, officers in the prison102 are able to place the arrested individual in a particular cell blockor housing unit of the prison 102 according to rules of thejurisdiction. For example, an officer may place an arrested individualaffiliated with a first gang in a cell block that does not includecriminals who are members of the first gang in order to separate thegang members in the prison 102. In another example, the officer mayplace the arrested individual affiliated with the first gang in a cellblock with criminals who are all members of the first gang in order toprevent the arrested individual (or other criminals in the cell block)from participating in altercations or fights with criminals who are gangmembers of a rival gang. By identifying tattoos, piercings, scars,brandings, surgical modifications, and/or identifying marks andproviding notifications to officers regarding gang-affiliations ofarrested individuals, the HD camera and image recognition system allowsprisons to attain criminal identities of arrested individuals quicklyand manage the placement or locations of criminals in the prison basedon their gang affiliations.

Additional functional components stored in the computer-readable media304 include an operating system 330 for controlling and managing variousfunctions of the image recognition server 300. The image recognitionserver 300 also includes or maintains other functional components anddata, such as other modules and data, which include programs, drivers,and the like, and the data used or generated by the functionalcomponents. Further, the image recognition server 300 includes manyother logical, programmatic and physical components, of which thosedescribed above are merely examples that are related to the discussionherein.

The communication interface(s) 306 include one or more interfaces andhardware components for enabling communication with various otherdevices, including cameras 132, 204 and/or 214, computing devices 134,investigator user devices 142, or other computing devices over network110. For example, communication interface(s) 306 facilitatecommunication through one or more of the Internet, cable networks,cellular networks, wireless networks (e.g., Wi-Fi, cellular) and wirednetworks. As several examples, the image recognition server 300 andother devices communicate and interact with one another using anycombination of suitable communication and networking protocols, such asInternet protocol (IP), transmission control protocol (TCP), hypertexttransfer protocol (HTTP), cellular or radio communication protocols, andso forth. Examples of communication interface(s) include a modem, anetwork interface (such as an Ethernet card), a communications port, aPCMCIA slot and card, and the like. The image recognition server 300 mayfurther be equipped with various input/output (I/O) devices 332. SuchI/O devices include a display, various user interface controls (e.g.,buttons, joystick, keyboard, mouse, touch screen, and the like), audiospeakers, connection ports and so forth.

Investigator User Device

FIG. 4 illustrates a diagram of an investigator user device 400,according to embodiments of the present disclosure. In some embodiments,the investigator user device 400 represents an exemplary embodiment ofinvestigator user device 142 in FIG. 1 . The investigator user device400 is operated by an individual, such as an investigator, detective,prosecutor, police officer, or administrator who is authorized toinvestigate suspects or individuals who are involved in criminalactivities.

As shown in FIG. 4 , the investigator user device 400 comprises adisplay 402 which illustrates an example user interface after a matchhas been found between data regarding a suspect and stored dataregarding an arrested individual. For example, the investigator userdevice 400 transmits suspect data to the image recognition server 300,in which the suspect data includes data regarding at least one of ashape, color, size, position, or intensity of a tattoo or identifyingmark of a suspect. In some cases, the suspect data includes at least oneof a photograph of the suspect or a description of the suspect providedby an investigator. The image recognition server 300 determines if thesuspect data received from the investigator user device 400 matches apreviously stored photograph of an arrested individual.

Upon detecting a match of the suspect data with feature datacorresponding to a previously arrested individual, the image recognitionserver 300 transmits an alert to one or more computing devices (e.g.,computing devices 134) in the prison. Transmission of the alert and/ordelivery of the alert to the one or more computing devices activates amobile application on the investigator user device 400 to execute on theinvestigator user device 400. For example, the alert received by one ormore computing devices in the prison activates an image recognitionmobile application to execute on the investigator user device 400, andthe image recognition mobile application opens up on a correspondinguser interface shown on the display 402.

After the image recognition mobile application opens on the display 402of the investigator user device 400, the user interface of display 402presents the investigator with an alert 404. For example, the alert 404comprises a push notification, message, and/or alert that provides matchdata identifying the suspected individual as a previously arrestedindividual, in which the match data includes at least one of a name,photograph, address, or other information corresponding to thepreviously arrested individual. For example, the alert 404 provides aphotograph and indicates the identity of the suspect to be “John Smith”who may be an individual previously arrested and photographed duringbooking at a prison by HD cameras (e.g., cameras 132, cameras 204, orcamera 214).

The alert 404 further provides the investigator with different optionsfrom which the investigator can choose. For example, the alert 404includes two options, such as a first option for “View details” in whichthe investigator can obtain additional details and/or match data on theidentified suspect, and a second option for “Send data” in which theinvestigator can choose to send the match data, including the identityof the suspect, to another investigator, detective, prosecutor, policeofficer, or administrator who is authorized and/or involved in theinvestigation. In other embodiments, the alert 404 indicates that amatch has not been found between a photograph of a suspect and aphotograph of an arrested individual. By providing investigators withupdated information regarding identities of suspected individualsthrough a mobile application, the HD camera and image recognition systemidentifies criminals for ongoing investigations in an efficient andtimely process.

System Operation

Operations of determining identities of suspected individuals bymatching features of arrested individuals and identifying features ofarrested individuals in a prison through HD camera and image recognitionsystem 100 will be described with respect to FIGS. 5 and 6 . Althoughthe physical devices and components that form the system have largelyalready been described, additional details regarding their more nuancedoperation will be described below with respect to FIGS. 1-4 . WhileFIGS. 5 and 6 contain methods of operation of determining identities ofsuspected individuals and identifying features of arrested individualsthrough the HD camera and image recognition system 100, the operationsare not limited to the order described below, and various operations canbe performed in a different order. Further, two or more operations ofeach method can be performed simultaneously with each other.

FIG. 5 illustrates a flowchart diagram of a method 500 of determiningidentities of suspected individuals by matching features of arrestedindividuals, via an image recognition server of an HD camera and imagerecognition system, such as via image recognition server 300 of FIG. 3 ,according to embodiments of the present disclosure. The steps of method500 are performed by modules of image recognition server 300, such ascriminal profile module 308, image processing module 310, featureextraction module 312, hashing module 314, pattern recognition module318, and/or notification module 328. Method 500 of FIG. 5 begins withstep 501 of receiving a plurality of photographs of an arrestedindividual. For example, image processing module 310 of imagerecognition server 300 receives a plurality of photographs of anarrested individual, in which the photographs have been captured by oneor more cameras, such as cameras 132, 204, and/or 214 in prison 102. Insome embodiments, the image processing module 310 receives the pluralityof photographs directly from at least one camera, such as cameras 132,cameras 204, or camera 214. In other embodiments, image processingmodule 310 receives the plurality of photographs from a computing device134 in the prison 102.

At step 502, image recognition server performs image processing of eachphotograph to extract a first set of features from the plurality ofphotographs. For example, the image processing module 310 of the imagerecognition server 300 performs pre-processing of the photographs, suchas by formatting, edits, and/or applying one or more digital imageprocessing algorithms to each photograph. After the photographs havebeen pre-processed, the feature extraction module 312 of the imagerecognition server 300 receives the plurality of photographs from imageprocessing module 310, and the feature extraction module 312 analyzesthe photographs to identify a first set of physical features relating tothe arrested individual. At step 503, the image recognition serverstores feature data regarding the first set of features in a database.For example, the hashing module 314 of the image recognition server 300performs cryptographic hashing to transform the feature data to hashvalues, and the hashing module 314 stores the hash value for eachfeature in booking database 316, in which the hash values are associatedwith the arrested individual.

At step 504, the image recognition server receives suspect dataregarding a suspected individual from an investigator user device. Forexample, the pattern recognition module 318 of the image recognitionserver 300 receives suspect data from investigator user device 142 or400, in which the suspect data includes data regarding at least one of ashape, color, size, position, or intensity of a tattoo or identifyingmark of a suspect. At step 505, the image recognition server determineswhether the suspect data matches the feature data. For example, thepattern recognition module 318 of the image recognition server 300determines whether the suspect data matches the feature data stored inthe booking database 316 by comparing a number of data points in thesuspect data with a number of data points in the feature data by usingat least one of pattern recognition algorithms, matching algorithms,biometric tattoo recognition algorithms, Hidden Markov models, and thelike.

If the image recognition server determines that the suspect data matchesthe feature data, then method 500 in this example proceeds to step 506.At step 506, the image recognition server transmits an alert to acomputing device that activates a mobile application on an investigatoruser device to display match data. For example, in response to matchingthe suspect data with the feature data, the pattern recognition module318 of image recognition server 300 communicates with the notificationmodule 328 to provide information regarding the match, including matchdata identifying the suspected individual as a previously arrestedindividual. In response to receiving the information, notificationmodule 328 transmits an alert to one or more computing devices 134 inthe prison 102. Transmission of the alert and/or delivery of the alertto the one or more computing devices 134 activates a mobile applicationinstalled on each investigator user device 142 to execute on eachinvestigator user device 142, and the mobile application opens up on theuser interface of each investigator user device 142 and displays acorresponding alert. The alert shown on the investigator user device 142includes match data identifying a suspected individual as a previouslyarrested individual along with at least one of a name, photograph,address, or other information corresponding to the previously arrestedindividual.

If the image recognition server determines that the suspect data doesnot match the feature data, then method 500 in this example proceeds tostep 507. At step 507, the image recognition server transmits an alertto a computing device indicating no match. For example, the patternrecognition module 318 of image recognition server 300 communicates withthe notification module 328 to provide information indicating that amatch has not been detected between the suspect data and the featuredata in the booking database 316. The notification module 328 thentransmits an alert to one or more computing devices 134 in the prison102 to notify the officers in the prison 102 that a match has not beenfound for the identity of the suspected individual. In some cases, theofficers utilizes one or more computing devices 134 in the prison 102 totransmit requests to the investigator user device 142 for additionalinformation or details regarding the suspected individual to furtherupdate a search for the suspect's identity.

FIG. 6 illustrates a flowchart diagram of a method 600 of identifyingfeatures of arrested individuals through image processing, via an imagerecognition server of an HD camera and image recognition system, such asvia image recognition server 300 of FIG. 3 , according to embodiments ofthe present disclosure. The steps of method 600 are performed by modulesof image recognition server 300, such as criminal profile module 308,image processing module 310, feature extraction module 312, hashingmodule 314, pattern recognition module 318, and/or notification module328. Method 600 of FIG. 6 begins with step 601 of receiving a pluralityof photographs captured by a camera. For example, image processingmodule 310 of image recognition server 300 receives a plurality ofphotographs of an arrested individual, in which the photographs havebeen captured by one or more cameras, such as cameras 132, 204, and/or214 in prison 102. In some embodiments, the image processing module 310receives the plurality of photographs directly from at least one camera,such as cameras 132, cameras 204, or camera 214.

At step 602, the image recognition server analyzes the pixels of eachphotograph in the plurality of photographs. For example, the imageprocessing module 310 of image recognition server 300 performspre-processing of the photographs and transmits the pre-processedphotographs to the feature extraction module 312. The feature extractionmodule 312 then analyzes the pixels of each photograph in the pluralityof photographs to identify pixel intensity values. At step 603, theimage recognition server detects regions of pixels in each photographthat differ in color and intensity values by predetermined thresholdvalues from adjacent regions of pixels. For example, feature extractionmodule 312 compares intensity values of pixels with intensity values ofneighboring pixels to identify similarities and differences betweenregions in each photograph. In some cases, feature extraction module 312detects that a region of neighboring pixels has similar intensity valueswith an adjacent region of neighboring pixels, in which the similarcolor and/or intensity values indicates the detection of flesh tones(e.g., untattooed or unmarked skin) of the arrested individual's bodyrepresented in the two regions of pixels.

For each photograph, the feature extraction module 312 continues toanalyze and compare each region of pixels with adjacent regions untilthe color and/or intensity values of a first region of pixels differfrom the color and/or intensity values of a second region of pixels byat least one or more predetermined threshold values, in which thediffering color and/or intensity values indicate the detection of atattoo, physical marking, and/or scar on the arrested individual's body.At step 604, the image recognition server identifies the regions ofpixels in each photograph that differ in color and/or intensity valuesas a first set of physical features relating to the arrested individual.For example, the feature extraction module 312 of image recognitionserver 300 detects the regions of pixels that differ in color and/orintensity values and identifies the detected regions as a first set ofphysical features comprising data regarding visual attributes of thearrested individual. The visual attributes include tattoos, physicalmarkings, and/or scars on the body of the arrested individual, and thedata regarding the visual attributes includes one or more valuescorresponding to shape, color, size, position, intensity, and the likeof the visual attributes of the arrested individual.

At step 605, the image recognition server stores the feature data in adatabase by cryptographic hashing. For example, the feature extractionmodule 312 provides the extracted feature data to the hashing module 314of image recognition server 300, and the hashing module 314 performscryptographic hashing to transform the feature data to hash values. Thehashing module 314 further stores the hash value for each feature inbooking database 316, in which the hash values are associated withcriminal profile data corresponding to the arrested individual. Inadditional embodiments, the pattern recognition module 318 accesses thehash values and feature data for the arrested individual and appliespattern recognition algorithms to determine that the features match atattoo, image, symbol, sign, or slogan representing a gang affiliation.By recognizing gang affiliations through tattoo detection, the imagerecognition server 300 of the HD camera and image recognition system 100identifies criminals quickly and efficiently for booking andinvestigation purposes.

Exemplary Computer Implementation

It will be apparent to persons skilled in the relevant art(s) thatvarious elements and features of the present disclosure, as describedherein, can be implemented in hardware using analog and/or digitalcircuits, in software, through the execution of computer instructions byone or more general purpose or special-purpose processors, or as acombination of hardware and software.

The following description of a general purpose computer system isprovided for the sake of completeness. Embodiments of the presentdisclosure can be implemented in hardware, or as a combination ofsoftware and hardware. Consequently, embodiments of the disclosure maybe implemented in the environment of a computer system or otherprocessing system. For example, the methods of FIGS. 4-5 can beimplemented in the environment of one or more computer systems or otherprocessing systems. An example of such a computer system 700 is shown inFIG. 7 . One or more of the modules depicted in the previous figures canbe at least partially implemented on one or more distinct computersystems 700.

Computer system 700 includes one or more processors, such as processor704. Processor 704 can be a special purpose or a general purpose digitalsignal processor. Processor 704 is connected to a communicationinfrastructure 702 (for example, a bus or network). Various softwareimplementations are described in terms of this exemplary computersystem. After reading this description, it will become apparent to aperson skilled in the relevant art(s) how to implement the disclosureusing other computer systems and/or computer architectures.

Computer system 700 also includes a main memory 706, preferably randomaccess memory (RAM), and may also include a secondary memory 708.Secondary memory 708 may include, for example, a hard disk drive 710and/or a removable storage drive 712, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, or the like. Removablestorage drive 712 reads from and/or writes to a removable storage unit716 in a well-known manner. Removable storage unit 716 represents afloppy disk, magnetic tape, optical disk, or the like, which is read byand written to by removable storage drive 712. As will be appreciated bypersons skilled in the relevant art(s), removable storage unit 716includes a computer usable storage medium having stored therein computersoftware and/or data.

In alternative implementations, secondary memory 708 may include othersimilar means for allowing computer programs or other instructions to beloaded into computer system 700. Such means may include, for example, aremovable storage unit 718 and an interface 714. Examples of such meansmay include a program cartridge and cartridge interface (such as thatfound in video game devices), a removable memory chip (such as an EPROM,or PROM) and associated socket, a thumb drive and USB port, and otherremovable storage units 718 and interfaces 714 which allow software anddata to be transferred from removable storage unit 718 to computersystem 700.

Computer system 700 may also include a communications interface 720.Communications interface 720 allows software and data to be transferredbetween computer system 700 and external devices. Examples ofcommunications interface 720 may include a modem, a network interface(such as an Ethernet card), a communications port, a PCMCIA slot andcard, etc. Software and data transferred via communications interface720 are in the form of signals which may be electronic, electromagnetic,optical, or other signals capable of being received by communicationsinterface 720. These signals are provided to communications interface720 via a communications path 722. Communications path 722 carriessignals and may be implemented using wire or cable, fiber optics, aphone line, a cellular phone link, an RF link and other communicationschannels.

As used herein, the terms “computer program medium” and “computerreadable medium” are used to generally refer to tangible storage mediasuch as removable storage units 716 and 718 or a hard disk installed inhard disk drive 710. These computer program products are means forproviding software to computer system 700.

Computer programs (also called computer control logic) are stored inmain memory 706 and/or secondary memory 708. Computer programs may alsobe received via communications interface 720. Such computer programs,when executed, enable the computer system 700 to implement the presentdisclosure as discussed herein. In particular, the computer programs,when executed, enable processor 704 to implement the processes of thepresent disclosure, such as any of the methods described herein.Accordingly, such computer programs represent controllers of thecomputer system 700. Where the disclosure is implemented using software,the software may be stored in a computer program product and loaded intocomputer system 700 using removable storage drive 712, interface 714, orcommunications interface 720.

In another embodiment, features of the disclosure are implementedprimarily in hardware using, for example, hardware components such asapplication-specific integrated circuits (ASICs) and gate arrays.Implementation of a hardware state machine so as to perform thefunctions described herein will also be apparent to persons skilled inthe relevant art(s).

Conclusion

It is to be appreciated that the Detailed Description section, and notthe Abstract section, is intended to be used to interpret the claims.The Abstract section may set forth one or more, but not all exemplaryembodiments, and thus, is not intended to limit the disclosure and theappended claims in any way.

The disclosure has been described above with the aid of functionalbuilding blocks illustrating the implementation of specified functionsand relationships thereof. The boundaries of these functional buildingblocks have been arbitrarily defined herein for the convenience of thedescription. Alternate boundaries may be defined so long as thespecified functions and relationships thereof are appropriatelyperformed.

It will be apparent to those skilled in the relevant art(s) that variouschanges in form and detail can be made therein without departing fromthe spirit and scope of the disclosure. Thus, the disclosure should notbe limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A system for high definition (HD) imagerecognition of criminals, the system comprising: a plurality ofinvestigator user devices; an image recognition server comprising aprocessor and a memory, wherein the processor is configured to: analyzepixels of each photograph in a plurality of photographs; detect a regionof pixels in at least one of the plurality of photographs that differ incolor and intensity values by a predetermined threshold value from anadjacent region of pixels; and identify a bodily feature of anindividual based on the detecting, the bodily feature including at leastone of a tattoo, a body piercing, a surgical modification, a branding, aphysical marking, or a scar; a computing device in a prison; and adatabase that stores cryptographically hashed feature data for thebodily feature of the individual, wherein each bodily featurecorresponds to a distinct hash value in the cryptographically hashedfeature data, wherein the plurality of investigator user devices, theimage recognition server, the computing device in the prison, and thedatabase are communicatively coupled via a network, and wherein theprocessor of the image recognition server is configured to: receivesuspect data regarding a suspected individual from an investigator userdevice of the plurality of investigator user devices; compare thesuspect data with the cryptographically hashed feature data stored inthe database to determine a confidence level indicating a degree ofmatch of the suspect data with cryptographically hashed feature dataassociated with a first individual of the plurality of individuals; inresponse to finding a match, transmit an alert to the computing devicein the prison, the alert activating a mobile application on at least oneinvestigator user device of the plurality of investigator user devicesto display match data identifying the suspected individual as the firstindividual of the plurality of individuals based on thecryptographically hashed feature data associated with the firstindividual of the plurality of individuals, and providing aninvestigator a first option to view additional details related to thesuspected individual and a second option to send the match data toanother investigator; and in response to not finding a match, send analert to the computing device in the prison indicating no match foundand transmit a request from the computing device in the prison to atleast one investigator device of the plurality of investigator userdevices, for additional information or details regarding the suspectedindividual.
 2. The system of claim 1, wherein the suspect data comprisesdata regarding a shape, color, size, position, and intensity of a tattooor identifying mark for a suspect.
 3. The system of claim 1, wherein thematching of the suspect data with the cryptographically hashed featuredata further comprises: applying a pattern recognition algorithm to thesuspect data and the cryptographically hashed feature data stored in thedatabase.
 4. The system of claim 1, wherein the processor is furtherconfigured to: identify a gang affiliation of the first individual basedon the cryptographically hashed feature data; and match the suspect datawith the cryptographically hashed feature data of the first individualbased on the gang affiliation of the first individual.
 5. The system ofclaim 1, wherein the set of bodily features relating to the individualis extracted by image processing of a plurality of photographs of theindividual.
 6. The system of claim 5, further comprising: a plurality ofcameras, wherein each camera is configured to capture a photograph ofeach individual, resulting in the plurality of photographs of eachindividual.
 7. The system of claim 6, wherein the plurality of camerascomprise high definition (HD) cameras arranged in a room of the prisonand configured to capture the plurality of photographs of eachindividual from a plurality of angles in the room.
 8. The system ofclaim 1, wherein the plurality of investigator user devices comprisemobile devices associated with investigators, detectives, prosecutors,police officers, or administrators authorized to investigate suspects.9. The system of claim 1, wherein the image recognition server isfurther configured to compare a number of data points in the suspectdata with a number of data points in the feature data by using at leastone of pattern recognition algorithms, matching algorithms, biometrictattoo recognition algorithms, and Hidden Markov models.
 10. An imagerecognition server for identifying an individual in a prison, the imagerecognition server comprising: a database that stores cryptographicallyhashed feature data for a bodily feature of an individual, wherein eachbodily feature corresponds to a distinct hash value in thecryptographically hashed feature data; a network interface deviceconfigured to communicate with the database, a computing device in theprison, and a plurality of investigator user devices; and a processorconfigured to: extract bodily feature data of an individual by analyzingpixels of a photograph from a plurality of photographs of the individualand further detecting a region of pixels in the photograph that differin color and intensity values by a predetermined threshold value from anadjacent region of pixels; and identify the bodily feature of anindividual based on the detecting, the bodily feature including at leastone of a tattoo, a body piercing, a surgical modification, a branding, aphysical marking, or a scar; store the extracted feature data of theindividual in the database; receive suspect data regarding a suspectedindividual from an investigator user device of the plurality ofinvestigator devices; determine whether the suspect data matches thecryptographically hashed feature data in the database based on aconfidence level indicating a degree of a match of the suspect data withcryptographically hashed feature data associated with an individual ofthe plurality of individuals; and transmit an alert to the computingdevice in the prison based on the determination, the alert activating amobile application on at least one investigator user device of aplurality of investigator user devices to display match data identifyingthe suspected individual as the first individual of the plurality ofindividuals based on the cryptographically hashed feature dataassociated with the first individual of the plurality of individuals,and providing an investigator a first option to view additional detailsrelated to the suspected individual and a second option to send thematch data to another investigator; and in response to not finding amatch, send an alert to the computing device in the prison indicating nomatch found and transmit a request from the computing device in theprison to at least one investigator device of the plurality ofinvestigator user devices, for additional information or detailsregarding the suspected individual.
 11. The image recognition server ofclaim 10, wherein the suspect data comprises data regarding a shape,color, size, position, and intensity of a tattoo or identifying mark fora suspect.
 12. The image recognition server of claim 10, wherein todetermine whether the suspect data matches the cryptographically hashedfeature data in the database, the processor is configured to: match thesuspect data with the cryptographically hashed feature data stored inthe database by applying one or more pattern recognition algorithms tothe suspect data and the cryptographically hashed feature data.
 13. Theimage recognition server of claim 10, wherein to transmit the alert tothe computing device in the prison based on the determination, theprocessor is configured to: transmit the alert to the computing devicein the prison in response to determining that the suspect data matchesthe cryptographically hashed feature data in the database, wherein thealert activates a mobile application on the investigator user device ofthe plurality of investigator user devices to display match dataidentifying the suspected individual as the individual of the pluralityof individuals based on the cryptographically hashed feature data. 14.The image recognition server of claim 10, wherein to transmit the alertto the computing device in the prison based on the determination, theprocessor is configured to: transmit the alert to the computing devicein the prison indicating no match in response to determining that thesuspect data does not match the cryptographically hashed feature data inthe database.
 15. The image recognition server of claim 10, wherein theinvestigator user device comprises a mobile device associated with aninvestigator, detective, prosecutor, police officer, or administratorauthorized to investigate the suspected individual.
 16. A method forimage recognition of criminals during booking at a prison, the methodcomprising: extracting bodily feature data of an individual by analyzingpixels of a photograph from a plurality of photographs of the individualand further detecting a region of pixels in the photograph that differin color and intensity values by a predetermined threshold value from anadjacent region of pixels; identifying a bodily feature of an individualbased on the detecting, the bodily feature including at least one of atattoo, a body piercing, a surgical modification, a branding, a physicalmarking, or a scar; receiving, by an image recognition server, suspectdata regarding a suspected individual from an investigator user deviceof a plurality of investigator user devices; comparing, by a processorof the image recognition server, the suspect data with cryptographicallyhashed feature data for the bodily feature of the individual stored in adatabase to determine a confidence level indicating a degree of match ofthe suspect data with cryptographically hashed feature data associatedwith a first individual, wherein each bodily feature corresponds to adistinct hash value in the cryptographically hashed feature data; and inresponse to finding a match, transmitting, by the processor, an alert toa computing device in the prison, the alert activating a mobileapplication on at least one investigator user device of the plurality ofinvestigator user devices to display match data identifying thesuspected individual as the first individual based on thecryptographically hashed feature data associated with the firstindividual of the plurality of individuals, and providing aninvestigator a first option to view additional details related to thesuspected individual and a second option to send the match data toanother investigator; and in response to not finding a match, sending analert to the computing device in the prison indicating no match foundand transmitting a request from the computing device in the prison to atleast one investigator device of the plurality of investigator userdevices, for additional information or details regarding the suspectedindividual.
 17. The method of claim 16, further comprising: capturing aplurality of photographs of the individual using a plurality of camerasarranged in a booking room of the prison; and extracting, by theprocessor, the set of bodily features relating to the individual byperforming image processing of the plurality of photographs of theindividual.
 18. The method of claim 17, wherein capturing the pluralityof photographs comprises: capturing the plurality of photographs from aplurality of angles in the booking room.
 19. The method of claim 17,wherein performing image processing of the plurality of photographs ofthe individual comprises: analyzing pixels of each photograph in theplurality of photographs; detecting a region of pixels in at least oneof the plurality of photographs that differ in color and intensityvalues by a predetermined threshold value from an adjacent region ofpixels; and identifying the region of pixels as a first set of bodilyfeatures, wherein the first set of bodily features comprises dataregarding visual attributes of the individual, and wherein the visualattributes of the first individual comprise tattoos, piercings, scars,or markings on a body of the first individual.
 20. The method of claim16, further comprising comparing, by the image recognition server, anumber of data points in the suspect data with a number of data pointsin the feature data by using at least one of pattern recognitionalgorithms, matching algorithms, biometric tattoo recognitionalgorithms, matching algorithms, and Hidden Markov models.